“AI is not sort of magic pixie dust that makes these problems go away. All it does is automate a human’s poorly defined goals with all the assumptions and biases therein.”

He makes the point that what we need is algorithm transparency – to make clear how the automated decisions were made, and what factors weighed in: “Essentially it’s a call for explaining what data is collected and how it is used.”

The challenge in the AI field is no one knows precisely how to provide this transparency – and sometimes the software authors have self-interest working against their willingness to share their algorithms. This problem is rampant with bots and other “AI” that are all the rage right now.

In BPM (Business Process Management), by contrast, we have a very good understanding of how to provide transparency. We have modeling languages designed to communicate how business processes work with less ambiguity and more transparency than ever before:

BPMN (Business Process Modeling Notation)

CMMN (Case Management Modeling Notation)

DMN (Decision Management Notation)

And there are still others that can be brought to bear as needed. Moreover, these models aren’t just conceptual – BPMN, as a case in point, is clear enough to be interpreted and run by a BPM Engine – a class of software readily available from major software vendors as well as upstarts.

Transparency – and understanding – were primary goals of these modeling initiatives. And while many in the AI world may resist the call for transparency that modeling paradigms might provide, if the BPM market is a guide, these technologies will really spread more widely with transparency. When you have Gartner and Forrester rating your solutions based on their adoption of an OMG spec, you’ll see vendors paying more attention to it… at least from software vendors if not from end users of the technology.

Meanwhile, if you’re looking to see through the magic pixie dust in your business applications, try leveraging solutions built on top of BPM technologies – that allow you to not only understand the underlying processes, but improve them.